Conversational AI & Language Engineering Lead

Bank of America•Chicago, IL

2d•$122,000 - $200,000•Onsite

About The Position

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day. Being a Great Place to Work is core to how we drive Responsible Growth. This includes our commitment to being an inclusive workplace, attracting and developing exceptional talent, supporting our teammates’ physical, emotional, and financial wellness, recognizing and rewarding performance, and how we make an impact in the communities we serve. Bank of America is committed to an in-office culture with specific requirements for office-based attendance and which allows for an appropriate level of flexibility for our teammates and businesses based on role-specific considerations. At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us! Job Description: This job is responsible for defining and leading the engineering approach for complex features to deliver significant business outcomes. Key responsibilities of the job include delivering complex features and technology, enabling development efficiencies, providing technical thought leadership based on conducting multiple software implementations, and applying both depth and breadth in a number of technical competencies. Additionally, this job is accountable for end-to-end solution design and delivery. Role Summary The Senior Technology Manager for Conversational AI & Language Engineering is a senior leadership role responsible for setting the technical vision, managing teams, and ensuring the long‑term health and performance of the machine learning capabilities powering Bank of America’s employee‑facing Virtual Assistant. This role is leadership‑first: accountable for strategy, governance, prioritization, and outcomes across natural language understanding (NLU), speech recognition, and LLM‑based capabilities.

Requirements

Automation
Influence
Result Orientation
Stakeholder Management
Technical Strategy Development
Application Development
Architecture
Business Acumen
Risk Management
Solution Design
Agile Practices
Analytical Thinking
Collaboration
Data Management
Solution Delivery
Process
Proven experience leading teams delivering conversational AI, NLP, or language‑driven ML solutions.
Strong background in natural language processing, intent classification, and speech recognition, with the ability to guide others rather than execute all work personally.
Experience shaping and executing technical strategy across multiple domains and channels.
Familiarity with LLMs and GenAI, including their application, limitations, and governance considerations in enterprise environments.
Working knowledge of Python, ML workflows, and evaluation techniques sufficient to provide technical leadership and oversight.
Experience with Agile and DevOps operating models.
Strong analytical thinking, decision‑making, and executive communication skills.
Experience with conversational interfaces and natural language processing.
Experience training machine learning algorithms for data classification and/or speech recognition.
Experience improving intent recognition of a data classification model.
Experience with python.
Unique skillset in computational linguistics and technical experience.
Familiarity with LLMs.
Familiarity with using version control technologies such as git, svn, or JIRA.
Experience in DevOps and Agile methodology.
Strong analytical and troubleshooting skills.

Responsibilities

Ensures that the design and engineering approach for complex features are consistent with the larger portfolio solution
Define the technology tool stack for the solution and evaluate and adapt new testing tool/framework/practices for team(s)
Enables team(s)/applications with Continuous Integration/Continuous Development (CI/CD) capabilities and engages with other technical stakeholders pertaining to efficient functioning of CI-CD pipeline
Guides and influences team(s) on design and best practices for high code performance –e.g. pairing, code reviews
Provides end-to-end delivery of complex features, including automation, for either a single team or multiple teams, at the program level
Conducts research, design prototyping and other exploration activities such as evaluating new toolsets and components for release management, CI/CD, and features
Works with stakeholders to establish high-level solution needs and with architects for technical requirements
Own the end‑to‑end technical strategy for language engineering, NLU, and conversational AI across web, mobile, and voice channels.
Define and evolve the intent portfolio and domain coverage (e.g., technology, human resources), ensuring alignment between conversation design, business needs, and ML capabilities.
Establish standards and best practices for model quality, evaluation, telemetry, and lifecycle management.
Provide senior‑level technical oversight for model design, training approaches, performance tradeoffs, and production readiness.
Serve as the accountable owner for model governance, including current and future registered models.
Help transition a mature deterministic solution with over 650 capabilities to leverage Generative and Agentic AI.
Lead and develop a specialized, medium‑sized team of language engineers and ML practitioners.
Set clear priorities, expectations, and success metrics for the team, balancing delivery, quality, and innovation.
Coach and mentor senior and mid‑level engineers, raising the overall technical bar and creating clear growth paths.
Ensure sustainable operating models that support scale, reliability, and continuous improvement.
Define the conversational AI roadmap, integrating traditional NLU techniques with LLM and GenAI capabilities where appropriate.
Partner with product owners, UX researchers, data scientists, and engineering leaders to shape the “brain” of the virtual assistant.
Translate complex technical topics into clear, executive‑level communication for stakeholders and leadership.
Identify systemic gaps or underperforming areas through analytics and conversation monitoring, and drive multi‑quarter improvement plans.